Use cases of the digital twin in supply chain management

The digital twin has arrived in the industrial supply chain. This statement is our conclusion from numerous consulting projects and market observations. According to the latest Gartner study “Future of Supply Chain Technology”, the importance of technology will continue to increase over the next five years and will be crucial to the success of four out of five companies.

The advantages of the technology in strategic alignment, tactical planning and operational execution of the supply chain are decisive for many users. Nevertheless, there are still a large number of companies that do not or hardly use the potential of this technology.
We have compiled a compendium of 11 industrial use cases for both. They show how diverse the use and benefits of the digital twin are in the supply chain. In order to enable general transferability of the statements, we have combined similar individual applications into general use cases and organized them according to the SCOR phases and the relevant industries. We address three important questions in a management-oriented manner:

• What is the purpose of the Digital Twin?
• What specific data is required?
• What is the benefit?

The use cases provide insight and inspiration for using the technology in your own company. If you would like to find out more about the use cases or the benefits of the Digital Supply Chain Twin, please contact us.

Optimization and stress testing of delivery schedules for procurement

Industries:

• Mechanical and plant engineering
• Metal processing
• Automation and electronics
• Automotive and agricultural technology
• Aerospace and defense
• Consumer goods (FMCG) and retail
• Pharmaceuticals, chemicals and raw materials
• Building and construction suppliers

What problem is being addressed?

A company wants to optimally and robustly align its procurement and inventories with material consumption and customer requirements. The inbound logistics network and external risk factors should be taken into account.

• Supplier network with capacities, delivery times, purchase prices and other parameters
• Allocation of orders to the existing suppliers
• Simulation of the delivery schedule under changing conditions (fluctuating customer demand, supplier failure, delays and failures in transportation) and adaptation to increase resilience

What specific data is required?

In addition to the master data on locations and materials, the following specific data and information is required for this use case:

• Supplier list
• Purchase prices, price scales, batch sizes
• Delivery times from the supplier to the plants
• Current stocks
• Material consumption or sales planning

How does the company benefit?

The company benefits in many ways:

• Cost and price advantages through improved cooperation with suppliers
• Control and coordination of incoming goods and smoothing of stock levels
• Increasing resilience in procurement

Production and distribution under allocation conditions

When customer demand temporarily exceeds a company’s production capacity, optimal production planning and product allocation are crucial for service levels and success.

Industries:

• Metal processing
• Automation and electronics
• Consumer goods (FMCG) and retail
• Pharmaceuticals, chemicals and raw materials
• Construction and construction suppliers
• Transportation and logistics

What problem is being addressed?

In an allocation phase, a company wants to maintain its service level and increase its earnings at the same time. Production capacities should be utilized and allocated in such a way that demand is met as comprehensively and cost-efficiently as possible and sales and contribution margin potentials are taken into account in a balanced manner.

• Creation of a medium to long-term production and delivery plan
• Consideration of the costs for production and distribution as well as the achievable prices
• Development of an optimal stockpiling strategy in the distribution network

What specific data is required?

In addition to the master data on locations, customers and products, the following specific data and information is required for this use case:

• Available capacity, capacity requirements of the products as well as set-up times and batch sizes
• Demand from customers or markets
• Cost parameters for production, processing, storage and transportation

How does the company benefit?

The company can use its production capacity to maximize profits without losing control over the service level. In addition, different production plans can be developed and compared with each other in order to be able to react appropriately to operational and strategic challenges during the allocation phase.

The digital twin in a portfolio strategy for multi-level value creation networks

In a multi-level supply chain, inventories must be held at various nodes in order to balance out the various internal and external influencing factors. A digital twin is an efficient tool for developing an optimal inventory strategy.

Industries:

• Mechanical and plant engineering
• Automation and electronics
• Automotive and agricultural technology
• Consumer goods (FMCG) and retail
• Pharmaceuticals, chemicals and raw materials
• Construction and construction suppliers

What problem is being addressed?

A company strives to optimize inventory levels in its multi-level value creation network in terms of service and profit level. The aim is to compensate for delivery problems, production bottlenecks, fluctuations in demand, unforeseen events and non-harmonized processes within given limits and to develop strategies for specific situations.

• Consideration of opportunity costs of unfulfilled demand
• Stock points and stock values per product for different service and profit levels
• Evaluation and comparison of different portfolio strategies using scenario technology

What specific data is required?

In addition to the master data on locations, customers and products, the following specific data and information is required for this use case:

• Demand from customers or markets
• Standard deviations of demand, order sizes, lead times and supplier performance based on historical data, forecasts or expected values

How does the company benefit?

The company gains transparency about the effectiveness of its inventories in terms of service and profit level. On this basis, an optimal inventory strategy can be developed and continuously adapted to best meet the company’s individual objectives.

• Consideration of opportunity costs of unfulfilled demand
• Stock points and stock values per product for different service and profit levels
• Evaluation and comparison of different portfolio strategies using scenario technology

Investment decisions in multi-stage series production

A digital twin can also be used to make optimal investment decisions for production factors. The surrounding value creation network is included in the decision.

Industries:

• Metal processing
• (Micro) electronics
• Automotive and agricultural technology
• Consumer goods (FMCG) and retail
• Pharmaceuticals, chemicals and raw materials
• Construction and construction suppliers

What problem is being addressed?

A company operates series production in a multi-stage value creation network. It wants to make economically justified decisions on the allocation of production factors (e.g. machines, systems, employees, etc.) in order to maximize its profit while maintaining a service level in line with the market.

• Customer requirements are distributed over a period of time and follow an arbitrary course.
• Production factors (buildings, machines, systems and personnel) are allocated to the nodes of the value creation network. Each production factor includes a specific capacity and reliability specification.
• All production, storage and transportation processes have processing and throughput times.

What specific data is required?

In addition to the master data on locations, customers and products, the following specific data and information is required for this use case:

• Capacities, throughput and replenishment times and costs for all stages of the value chain (including suppliers and distribution)
• Capacities, throughput times and costs for the individual process steps within production
• Selling prices for the products in the individual markets
• Purchase prices of externally sourced raw materials and semi-finished goods
• Inventory and replenishment rules for all stages of the value chain

How does the company benefit?

The company can regularly evaluate the value creation network and make economically and operationally sound decisions on resource allocation in response to changing market requirements.

Supply chain-optimized batch size planning in the process industry

In this use case, a classic process simulation is combined with the digital twin of the supply chain. A special focus is placed on the process industry.

Industries:

• Consumer goods (FMCG) and retail
• Pharmaceuticals, chemicals and raw materials
• Energy and water supplier

What problem is being addressed?

A company in the process industry wants to adapt the batch size planning of its continuous production to the requirements of the discontinuous supply chain in procurement and distribution.

• Precise mapping of the continuous production process
• Consideration of the shelf life of raw materials, intermediate and finished products

What specific data is required?

In addition to the master data on locations and products, the following specific data and information is required for this use case:

• Historical customer requirements
• Delivery schedules and delivery times in procurement
• Parameters of the continuous production process Capacities, batch sizes, process times, product-specific changeover times, etc.

How does the company benefit?

The company can adjust its continuous production to suppliers and customers in a cost-optimized manner in terms of capacity utilization and degree of utilization of the initial products.

Summary

The digital twin has arrived in the industrial supply chain. The use cases presented show how diverse the possible applications of the technology are. The current focus is on make and deliver processes. At the same time, they only represent a section of the range of applications. Many companies have also realized that the benefits of the Digital Twin are particularly evident when it is regularly updated and continuously used for decision-making at an operational, tactical and strategic level. If you are interested in this article, download our whitepaper with 6 further use cases. Further information on the digital twin in operations can be found here.